Parvo ICU Volunteer Workforce
Hunter Ratliff, @HunterRatliff1
March 13, 2016
This analysis utilizes data from the Parvo ICU End of Shift Report submitted at the end of each shift. It has not been cross-validated against independent (and official) data sources like PetPoint of MyVolunteerPage, and therefore might contain some erroneous data points.
Using data submitted at the end of shifts via the Parvo ICU End of Shift Report, this analysis explores from the trends and patterns in the Parvo ICU over multiple years. Data spans from 2013-06-10 to 2016-03-27 and represents 1927 total observations (e.g. End of Shift submissions).
Volunteers
First, we’ll take a look at the volunteers and staff who were listed as a response to the Shift Staff/Volunteers question on the report. Responses have been modified to fix misspellings and standardize the format.
Responses lists names as a comma separated list, like the one below. The method of parsing names is fairly indiscreet, and identified a total of 102 unique names across all submissions. For the sake of simplicity, only names appearing 10 or more times have been counted. By applying this filter, the number of names counted was reduced to 57.
Additional complications arose from volunteers sharing the same first name. Sometimes submissions will specify which volunteer was present by including their last initial, but often this information is omitted. Therefore, there is a great deal of cross over among a few volunteers with common names, but unfortunatly this is unavoidable.
In cases where a volunteer/staff was listed as training a new volunteer (e.g. Katie training Hunter), both the trainer and trainee have been counted.
Volunteer, Volunteer, …
Volunteer, Trainer training Trainee
To begin, here’s a table of the top 25 volunteers, sorted by the number of shifts for which they’ve been present:
| Volunteer | n |
|---|---|
| Katie | 524 |
| Emily | 253 |
| Stasha | 214 |
| Emily M | 172 |
| Amy | 145 |
| Sara | 141 |
| Nipuni | 135 |
| Nicole | 131 |
| Hunter | 128 |
| Kumiko | 107 |
| Shelby | 99 |
| Sara C | 96 |
| Amanda | 94 |
| Michael | 93 |
| Peggy | 91 |
| Shanti | 89 |
| Keri | 86 |
| Michaela | 85 |
| Martha | 84 |
| Breanne | 83 |
| Will | 76 |
| Grace | 68 |
| Melissa | 68 |
| Erica | 66 |
| Genevieve | 61 |
Heatmaps
Who volunteers together
Below is a correlation matrix represented as a heatmap. The values (colors of the blocks) the correlation co-efficient of the number of shifts worked per week by using the pearson method. Higher values represent an increased likelihood that those two volunteers volunteered during the same week.
The numerical value is probably of little practical signifigance, but the general clustering (represented by “chunks” of similar colors, or by the dendograms on either side of the plot) do help to highlight different clusters of volunteers.
Who volunteers by day of the week and AM/PM
Next, we’ll look at another heatmap. This time, however, the numerical values are indicative of the number of shifts that each volunteer has been present for at each time of the day/week.
This figure is colored relative to each volunteer (i.e. by row), so it highlights what days/time each volunteer prefers. The most useful conclusion we may derive here is the noticable lack of crossover between AM and PM volunteers.
Violin/Boxplots
Engagement Over Time
This figure demonstrates the patterns of shifts by each volunteer (with more than 10 shifts total) over the duration of thier time as a Parvo volunteer/staff
Each point represents one shift (colored by how long the shift lasted). Compared to other volunteer oppurtunites at Austin Pets Alive!, Parvo actually has a very high reternion rate, especially among those who have been here for over a year.
Monthly Engagement
Another way to conceptualize this might be to look at the number of
Rolling Hours by days since start
# How About The Dogs?
First, we’ll run an unadjusted linear model based on the number of dogs at each level that are in the ICU
It’s difficult to extrapolate the seasonal trends based on two springs, but if we assume that this sinusoidal pattern were to continue, we should expect a huge peak this summer, since March is only the beginning of the upswing for Parvo season.
But obviously, there’s a whole lot of variance from the mean
Inventory/Supplies
Create a data.frame of all the Inventory requests
Tracking the request over time
## Source: local data frame [59 x 2]
##
## Supply n
## (chr) (int)
## 1 Turkey 319
## 2 Chicken Sticks 266
## 3 Variety Canned food 234
## 4 Baby Food 221
## 5 Bleach 158
## 6 Famotidine 156
## 7 Cefazolin/Ampicillin 146
## 8 Anzemet 141
## 9 LRS 121
## 10 3cc Syringes 117
## .. ... ...A pretty uneventful correlation matrix visualized as a heatmap
| r.squared | adj.r.squared | sigma | statistic | p.value | df |
|---|---|---|---|---|---|
| 0.879597 | 0.8792209 | 56.06913 | 2338.958 | 0 | 6 |
--- LICENSE ---
Copyright (C) 2016 Hunter Ratliff
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In the spirit of Reproducible Research, below is the information About the R Session at the time it was compiled:
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